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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021-08-04
摘要
Many complex 網絡s depend upon 生物 entities for their preservation. Such entities, from human cognition to 演化, must first encode and then replicate those 網絡s under marked resource constraints. 網絡s that survive are those that are amenable to constrained encoding-or, in other words, are compressible. But how compressible is a 網絡? And what features make one 網絡 more compressible than another? Here, we answer these questions by modeling 網絡s as 資訊 sources before compressing them using rate-distortion theory. Each 網絡 yields a unique rate-distortion curve, which specifies the minimal amount of 資訊 that remains at a given scale of description. A natural definition then emerges for the compressibility of a 網絡: the amount of 資訊 that can be removed via compression, averaged across all scales. Analyzing an
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